COVID-19 Prevention Facilitators and Barriers among Specific Ethnic Minority Communities in Rural Ohio

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Abstract

Objective

To assess knowledge, beliefs, and behaviors concerning COVID-19 among Guatemalan, Marshallese, and Amish populations in rural Ohio; identify individual, interpersonal, community, and structural level challenges within each community; and provide population-specific recommendations to prevent and mitigate further SARS-CoV-2 transmission among these rural communities.

Methods

We conducted 30 key informant interviews in four rural counties in Ohio, in May 2020. Three teams of two investigators conducted interviews with local health department staff, community members, meat packing plant management, and community leaders from three communities disproportionately affected by the COVID-19 pandemic [Guatemalan (N=12), Marshallese (N=7), Amish (N=11)]. We used the Social Ecological Model to identify and categorize themes.

Results

Emerging and overall themes were identified and defined. Investigators identified COVID-19 knowledge gaps, myths, and misinformation, food insecurity, community cohesion, stigma, community culture and norms, lack of workplace safety policies, and access to testing as key themes to COVID-19 prevention.

Conclusions

Understanding specific barriers and identifying facilitators that most effectively provide resources, healthcare services, education, and social support tailored to specific communities would help deter SARS-CoV-2 transmission.

Article activity feed

  1. SciScore for 10.1101/2021.10.21.21265302: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: The study team obtained oral informed consent prior to interviews.
    IACUC: The study was reviewed by the CDC ethics committee and approved as non-research.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.